• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.11B no.4
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• pp.129-136
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• 2001

In order to evaluate the dielectric recovery strength for GCBs, two equations have been usually utilized. One is the empirical formula obtained from a series of tests and the other is the theoretical formula obtained from the streamer theory. In this paper, both methods were applied to predict the small capacitive current interruption capability of model circuit breakers and were investigated in terms of the reliability by comparing the simulation results with test ones.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.110-115
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• 2004

The hybrid fault diagnosis method based on a combination of the signed digraph (SDG) and the partial least-squares (PLS) has the advantage of improving the diagnosis resolution, accuracy and reliability, compared to those of previous qualitative methods, and of enhancing the ability to diagnose multiple fault. In this study, the method is applied for the multiple fault diagnosis of the Tennessee Eastman challenge process, which is a realistic industrial process for evaluating process contol and monitoring methods. The process is decomposed using the local qualitative relationships of each measured variable. Dynamic PLS (DPLS) model is built to estimate each measured variable, which is then compared with the estimated value in order to diagnose the fault. Through case studies of 15 single faults and 44 double faults, the proposed method demonstrated a good diagnosis capability compared with previous statistical methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.840-843
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• 1996

This paper presents an adaptive cutting force controller for milling process, which can be attached to most commercial CNC machining centers in a practical way. The cutting forces of X,Y and Z axes measured indirectly from the use of currents drawn by AC feed-drive servo motors. A typical model for the feed-drive control system of a horizontal machining center is developed to analyze cutting force measurement from the drive motor. The pulsating milling forces can be measured indirectly within the bandwidth of the current feedback control loop of the feed-drive system. It is shown that indirectly measured cutting force signals can be used in the adaptive controller for cutting force regulation. The robust controller structure is adopted in the whole adaptive control scheme. The conditions under which the whole scheme is globally convergent and stable are presented. The suggested control scheme has been implemented into a commercial machining center, and a series of cutting experiments on end milling and face milling processes are performed. The adaptive controller reveals reliable cutting force regulating capability under various cutting conditions.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• Smart Structures and Systems
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• v.19 no.1
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• pp.21-31
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• 2017

To control the stochastic vibration of a vibration-sensitive instrument supported on a beam, the beam is designed as a sandwich structure with magneto-rheological visco-elastomer (MRVE) core. The MRVE has dynamic properties such as stiffness and damping adjustable by applied magnetic fields. To achieve better vibration control effectiveness, the optimal bounded parametric control for the MRVE sandwich beam with supported mass under stochastic and deterministic support motion excitations is proposed, and the stochastic and shock vibration suppression capability of the optimally controlled beam with multi-mode coupling is studied. The dynamic behavior of MRVE core is described by the visco-elastic Kelvin-Voigt model with a controllable parameter dependent on applied magnetic fields, and the parameter is considered as an active bounded control. The partial differential equations for horizontal and vertical coupling motions of the sandwich beam are obtained and converted into the multi-mode coupling vibration equations with the bounded nonlinear parametric control according to the Galerkin method. The vibration equations and corresponding performance index construct the optimal bounded parametric control problem. Then the dynamical programming equation for the control problem is derived based on the dynamical programming principle. The optimal bounded parametric control law is obtained by solving the programming equation with the bounded control constraint. The controlled vibration responses of the MRVE sandwich beam under stochastic and shock excitations are obtained by substituting the optimal bounded control into the vibration equations and solving them. The further remarkable vibration suppression capability of the optimal bounded control compared with the passive control and the influence of the control parameters on the stochastic vibration suppression effectiveness are illustrated with numerical results. The proposed optimal bounded parametric control strategy is applicable to smart visco-elastic composite structures under deterministic and stochastic excitations for improving vibration control effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2243-2257
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• 2016

This paper presents a two stage three-phase proton exchange membrane fuel cell (PEMFC) generation system. When the system is connected to a three-phase load, it is very sensitive to the characteristics and type of the load. Especially unbalanced three-phase loads, which result in a pulsating power that is twice the output frequency at the inverter output, and cause the dc-link to generate low frequency ripples. This penetrates to the fuel cell side through the front-end dc-dc converter, which makes the fuel cell work in an unsafe condition and degrades its lifespan. In this paper, the generation and propagation mechanism of low frequency ripple is analyzed and its impact on fuel cells is presented based on the PEMFC output characteristics model. Then a novel method to evaluate low frequency current ripple control capability is investigated. Moreover, a control scheme with bandpass filter inserted into the current feed-forward path, and ripple duty ratio compensation based on current mode control with notch filter is also proposed to achieve low frequency ripple suppression and dynamic characteristics improvement during load transients. Finally, different control methods are verified and compared by simulation and experimental results.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.5
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• pp.366-372
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• 2018

Three-port dual-active bridge (DAB) converter in a DC microgrid was studied due to its high power density and cost-effectiveness. The other advantages of DAB include galvanic isolation and bidirectional power conversion capability using simple control modulation. The three-port DAB converter consists of a three winding transformer and three bridges. The transformer has three phases, which means that the ports are coupled. Thus, the three-port DAB converter causes unwanted power flows when the load connected to each port changes. The basic operational principles of the three-port DAB converter are presented in this study. The decoupling control strategy of the independent port power transfer is presented with a mathematical power model to overcome the unexpected power flow problem. The validity of the proposed analysis and control strategy is verified with PSIM simulation and experiments using a 1-kW prototype power converter.

• Smart Structures and Systems
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• v.13 no.5
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• pp.849-868
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• 2014

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.6
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• pp.497-504
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• 2012

Finite element analysis is to approximate a geometry model developed in computer-aided design(CAD) to a finite element model, thus the conventional shape design sensitivity analysis and optimization using the finite element method have some difficulties in the parameterization of geometry. However, isogeometric analysis is to build a geometry model and directly use the functions describing the geometry in analysis. Therefore, the geometric properties can be embedded in the NURBS basis functions and control points so that it has potential capability to overcome the aforementioned difficulties. In this study, the isogeometric structural analysis and shape design sensitivity analysis in the generalized curvilinear coordinate(GCC) systems are discussed for the curved geometry. Representing the higher order geometric information, such as normal, tangent and curvature, yields the isogeometric approach to be the best way for generating exact GCC systems from a given CAD geometry. The developed GCC isogeometric structural analysis and shape design sensitivity analysis are verified to show better accuracy and faster convergency by comparing with the results obtained from the conventional isogeometric method.